The fields of Machine Learning (ML) and Deep Learning (DL) are pivotal to the modern advancements in Artificial Intelligence (AI) and have introduced powerful capabilities for systems to learn from complex data. As these technologies continue to evolve rapidly, a comprehensive review of their foundational concepts, architectures, applications, and future trajectories is essential. This paper aims to provide a consolidated overview of the current state of ML and DL, highlighting key methodologies and emerging trends. We conducted a systematic review of the literature, focusing on the core paradigms of supervised, unsupervised, and reinforcement learning. The review details the standard ML/DL workflow from data preprocessing to deployment and examines the primary architectures of deep neural networks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Our analysis reveals that DL, through its multi-layered neural architectures, has enabled unprecedented success in tasks such as computer vision and natural language processing. The review identifies widespread applications across diverse sectors, including healthcare (e.g., medical imaging), finance (e.g., fraud detection), agriculture, and robotics. Furthermore, we highlight critical advancements and ongoing research priorities, such as Explainable AI (XAI) for enhancing model transparency, federated learning for privacy-preserving computation, and the growing focus on ethical AI to mitigate bias and ensure fairness. ML and DL are fundamental drivers of AI innovation, with applications that are reshaping industries globally. The future of these fields is marked by a focus on addressing practical challenges such as interpretability and ethical considerations, alongside exploring novel frontiers like quantum machine learning and Edge AI. This review underscores the profound impact of these technologies and points toward a future of more intelligent, transparent, and ethically-minded AI systems.